講演情報
[ACG64-03]A Phase-Insensitive Framework for Evaluating Terrestrial GPP Variability in CMIP6 Earth System Models
*佐藤 雄亮1、羽島 知洋1 (1.海洋研究開発機構)
キーワード：
経年変動性、陸域生態系プロセス、総一次生産
The interannual variability (IAV) in terrestrial carbon uptake dominates a large part of the variability in the atmospheric CO2 growth rate. Improving our understanding of the underlying terrestrial ecosystem processes that drive this variability is essential for reducing uncertainties in future carbon budget projections and is increasingly important for evaluating progress toward decarbonization efforts. Accordingly, an adequate representation of interannual variability in terrestrial ecosystem processes, such as gross primary production (GPP), is critical for Earth System Models (ESMs).
However, in many coupled ESM simulations participating in CMIP, the phase of internal climate variability does not align with that of the real world, making it fundamentally difficult to directly compare time-series of interannual variability. This limitation persists even when atmosphere-ocean data assimilation is applied and has hindered robust evaluation of ecosystem processes governing IAV. Consequently, most previous CMIP-based evaluations of the terrestrial carbon cycle have focused on mean states or climatological characteristics, while the representation of variability and ecosystem responses to extreme events remain insufficiently assessed.
Here, we propose a phase-insensitive model evaluation framework designed to minimize the influence of phase mismatches in internal variability. Within this framework, we examine the general relationships between environmental variability, such as precipitation and temperature, and GPP responses, with particular emphasis on extreme and nonlinear variations that strongly contribute to IAV. CMIP6 models are evaluated in terms of the IAV described as the distributional properties (variability structure) of GPP and climate drivers, as well as their relationships, through comparison with multi-source observational datasets. We further assess whether biases in simulated GPP primarily arise from biases in environmental drivers or from differences in ecosystem response structures.
To capture ecosystem response diversity that is not apparent in global-mean analyses, the evaluation is conducted at regional scales across multiple climate zones, focusing on regions exhibiting pronounced interannual variability in GPP. Model behavior is systematically assessed in terms of seasonality and environmental responses to better understand the underlying processes. Because variability at annual timescales is strongly influenced by ecosystem functioning at seasonal scales, the results show that monthly and seasonal diagnostics provide an effective basis for evaluating terrestrial ecosystem representations in ESMs with respect to IAV.
This study highlights both common features and inter-model differences in the representation of GPP variability across CMIP models and discusses implications for improving terrestrial ecosystem processes in Earth System Models.
However, in many coupled ESM simulations participating in CMIP, the phase of internal climate variability does not align with that of the real world, making it fundamentally difficult to directly compare time-series of interannual variability. This limitation persists even when atmosphere-ocean data assimilation is applied and has hindered robust evaluation of ecosystem processes governing IAV. Consequently, most previous CMIP-based evaluations of the terrestrial carbon cycle have focused on mean states or climatological characteristics, while the representation of variability and ecosystem responses to extreme events remain insufficiently assessed.
Here, we propose a phase-insensitive model evaluation framework designed to minimize the influence of phase mismatches in internal variability. Within this framework, we examine the general relationships between environmental variability, such as precipitation and temperature, and GPP responses, with particular emphasis on extreme and nonlinear variations that strongly contribute to IAV. CMIP6 models are evaluated in terms of the IAV described as the distributional properties (variability structure) of GPP and climate drivers, as well as their relationships, through comparison with multi-source observational datasets. We further assess whether biases in simulated GPP primarily arise from biases in environmental drivers or from differences in ecosystem response structures.
To capture ecosystem response diversity that is not apparent in global-mean analyses, the evaluation is conducted at regional scales across multiple climate zones, focusing on regions exhibiting pronounced interannual variability in GPP. Model behavior is systematically assessed in terms of seasonality and environmental responses to better understand the underlying processes. Because variability at annual timescales is strongly influenced by ecosystem functioning at seasonal scales, the results show that monthly and seasonal diagnostics provide an effective basis for evaluating terrestrial ecosystem representations in ESMs with respect to IAV.
This study highlights both common features and inter-model differences in the representation of GPP variability across CMIP models and discusses implications for improving terrestrial ecosystem processes in Earth System Models.